In order to reduce the number of variables and to lessen
potential multicollinearity problems, TMT, strategy, financial,
and industry data were factor analyzed to determine which items
could be combined. The varimax factor loadings are
presented in Table 1. An
eleven factor solution explained 47.3% of the variation among the
30 items. Based upon this factor structure, individual
items were multiplied by factor loadings and summed to calculate
variables for further analyses. Thus, items highly loaded
on each factor were combined to create new variables called
technical education (Factor 3), new venture experience (Factor
4), industry experience (Factor 7), finance experience (Factor
8), innovative differentiation (Factor 1), marketing
differentiation (Factor 6), low cost (Factor 2), strategic
breadth (Factor 5), liquidity (Factor 9), industry performance
(Factor 10), and industry technological change (Factor 11).
Because focus, debt to equity, ROS, and industry growth did not
load significantly on any of the factors, these variables were
used alone in further analyses.

Tables 2 and 3 present results of multiple
stepwise regressions used to examine factors related to venture
capital financing and firm performance. Each model was
significant at p<.001. Independent variables in the
study explained a substantial percentage of the variation in
whether or not ventures were funded by venture capitalists (Model
R2=.33), percentage of venture capital ownership (Model R2=.19),
sales growth (Model R2=.26) and EPS (Model
R2.30). Partial R2s were reported in Tables 2 and 3 in order to assess the relative
importance of the independent variables in explaining the
dependent variables.

Results in Table 2
indicate that compared to IPO firms without venture capital
backing, those with such backing were older (Partial R2=.18;
p<.001) and their management teams had higher levels of
technical education (Partial R2=.08; p<.001),
industry experience (Partial R2=.02; p<.05) and
finance experience (Partial R2=.02; p<.05).
Furthermore, they participated in industries with higher levels
of performance (Partial R2=.01; p<.1),
technological change (Partial R2=.01; p<.1) and
growth (Partial R2=.01; p<.1). The firm's age
at the time of the IPO was the most important variable in
distinguishing between funded and unfunded ventures (Partial R2=.18).
Management team characteristics were second most important
(Partial R2=.12), industry characteristics were only
mildly important (Partial R2=.03) and the strategies
and financial characteristics of the firms were not significantly
related to venture capital funding.

Results in Table 2 also
indicate that venture age was the most important indicator of the
percentage of venture capital ownership (Partial R2=.12;
p<.001), followed by technical education (Partial R2=.05;
p<.001) and industry performance (Partial R2=.02;
p<.05). Firms strategies and financial characteristics
were not significantly related to the percentage of venture
capital funding. The relative importance of independent
variables in explaining percentage of venture capitalist
ownership was the same as indicated above for venture capital
funding. Firm age explained the greatest variance, followed
by TMT background and industry characteristics. As with
venture capital funding, firm strategies and financial
characteristics were not associated with percentage of venture
capital ownership.